SoroushVahidi/scidocs huggingface dataset:
SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights
(2012)

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.
Can you fool AI with adversarial examples on a visual Turing test?
(2017)

Deep learning has achieved impressive results in many areas of Computer Vision and Natural Language Processing. Among others, Visual Question Answering (VQA), also referred to a visual Turing test, is considered one of the most compelling problems, and recent deep learning models have reported significant progress in vision and language modeling. Although Artificial Intelligence (AI) is getting closer to passing the visual Turing test, at the same time the existence of adversarial examples to deep learning systems may hinder the practical application of such systems. In this work, we conduct the first extensive study on adversarial examples for VQA systems. In particular, we focus on generating targeted adversarial examples for a VQA system while the target is considered to be a question-answer pair. Our evaluation shows that the success rate of whether a targeted adversarial example can be generated is mostly dependent on the choice of the target question-answer pair, and less on the choice of images to which the question refers. We also report the language prior phenomenon of a VQA model, which can explain why targeted adversarial examples are hard to generate for some question-answer targets. We also demonstrate that a compositional VQA architecture is slightly more resilient to adversarial attacks than a non-compositional one. Our study sheds new light on how to build deep vision and language resilient models robust against adversarial examples.
Designing a mobile game to thwarts malicious IT threats: A phishing threat avoidance perspective
(2015)

Phishing is an online identity theft, which aims to steal sensitive information such as username, password and online banking details from victims. To prevent this, phishing education needs to be considered. Game based education is becoming more and more popular. This paper introduces a mobile game prototype for the android platform based on a story, which simplifies and exaggerates real life. The elements of a game design framework for avoiding phishing attacks were used to address the game design issues and game design principles were used as a set of guidelines for structuring and presenting information. The overall mobile game design was aimed to enhance the user’s avoidance behaviour through motivation to protect themselves against phishing threats. The prototype mobile game design was presented on MIT App Inventor Emulator.
Fibonacci Exposure Bracketing for High Dynamic Range Imaging
(2013)

Exposure bracketing for high dynamic range (HDR) imaging involves capturing several images of the scene at different exposures. If either the camera or the scene moves during capture, the captured images must be registered. Large exposure differences between bracketed images lead to inaccurate registration, resulting in artifacts such as ghosting (multiple copies of scene objects) and blur. We present two techniques, one for image capture (Fibonacci exposure bracketing) and one for image registration (generalized registration), to prevent such motion-related artifacts. Fibonacci bracketing involves capturing a sequence of images such that each exposure time is the sum of the previous N(N > 1) exposures. Generalized registration involves estimating motion between sums of contiguous sets of frames, instead of between individual frames. Together, the two techniques ensure that motion is always estimated between frames of the same total exposure time. This results in HDR images and videos which have both a large dynamic range and minimal motion-related artifacts. We show, by results for several real-world indoor and outdoor scenes, that the proposed approach significantly outperforms several existing bracketing schemes.
Harvesting Mid-level Visual Concepts from Large-Scale Internet Images
(2013)

Obtaining effective mid-level representations has become an increasingly important task in computer vision. In this paper, we propose a fully automatic algorithm which harvests visual concepts from a large number of Internet images (more than a quarter of a million) using text-based queries. Existing approaches to visual concept learning from Internet images either rely on strong supervision with detailed manual annotations or learn image-level classifiers only. Here, we take the advantage of having massive well organized Google and Bing image data, visual concepts (around 14, 000) are automatically exploited from images using word-based queries. Using the learned visual concepts, we show state-of-the-art performances on a variety of benchmark datasets, which demonstrate the effectiveness of the learned mid-level representations: being able to generalize well to general natural images. Our method shows significant improvement over the competing systems in image classification, including those with strong supervision.
Viewpoint-independent book spine segmentation
(2014)

We propose a method to precisely segment books on bookshelves in images taken from general viewpoints. The proposed segmentation algorithm overcomes difficulties due to text and texture on book spines, various book orientations under perspective projection, and book proximity. A shape dependent active contour is used as a first step to establish a set of book spine candidates. A subset of these candidates are selected using spatial constraints on the assembly of spine candidates by formulating the selection problem as the maximal weighted independent set (MWIS) of a graph. The segmented book spines may be used by recognition systems (e.g., library automation), or rendered in computer graphics applications. We also propose a novel application that uses the segmented book spines to assist users in bookshelf reorganization or to modify the image to create a bookshelf with a tidier look. Our method was successfully tested on challenging sets of images.
An improved fuzzy clustering method using modified Fukuyama-Sugeno cluster validity index
(2011)

The objective of clustering algorithms is to group similar patterns in one class and dissimilar patterns in disjoint classes. This article proposes a novel algorithm for fuzzy partitional clustering with an aim to minimize a composite objective function, defined using the Fukuyama-Sugeno cluster validity index. The optimization of this objective function tries to minimize the separation between clusters of a data set and maximize the compactness of a certain cluster. But in certain cases, such as a data set having overlapping clusters, this approach leads to poor clustering results. Thus we introduce a new parameter in the objective function which enables us to yield more accurate clustering results. The algorithm has been validated with some artificial and real world datasets.
Secure Conjunctive Keyword Search over Encrypted Data
(2004)

We study the setting in which a user stores encrypted documents (e.g. e-mails) on an untrusted server. In order to retrieve documents satisfying a certain search criterion, the user gives the server a capability that allows the server to identify exactly those documents. Work in this area has largely focused on search criteria consisting of a single keyword. If the user is actually interested in documents containing each of several keywords (conjunctive keyword search) the user must either give the server capabilities for each of the keywords individually and rely on an intersection calculation (by either the server or the user) to determine the correct set of documents, or alternatively, the user may store additional information on the server to facilitate such searches. Neither solution is desirable; the former enables the server to learn which documents match each individual keyword of the conjunctive search and the latter results in exponential storage if the user allows for searches on every set of keywords. We define a security model for conjunctive keyword search over encrypted data and present the first schemes for conducting such searches securely. We propose first a scheme for which the communication cost is linear in the number of documents, but that cost can be incurred “offline” before the conjunctive query is asked. The security of this scheme relies on the Decisional Diffie-Hellman (DDH) assumption. We propose a second scheme whose communication cost is on the order of the number of keyword fields and whose security relies on a new hardness assumption.
How to Enhance Cloud Architectures to Enable Cross-Federation: Towards Interoperable Storage Providers
(2010)

Small/medium cloud storage providers can hardly compete with the biggest cloud players such as Google, Amazon, Dropbox, etc. As a consequence, the cloud storage market depends on such mega-providers and each small/medium provider cannot face alone the challenge of Big Data storage. A possible solution consists in establishing stronger partnerships among small-medium providers where they can borrow/lend resources each other, according to the rules of the federated cloud ecosystem they belong to. According to such an approach, the challenge consists in creating federated cloud ecosystems able to compete with mega-provides and one of the major problems for the achievement of such an ecosystem is the management of inter-domain communications. In this paper, we propose an architecture addressing such an issue. In particular, we present and test a solution integrating the CLEVER Message Oriented Middleware (MOM) with the Hadoop Distribute File System (HDFS), i.e., one of the major massive storage solutions currently available on the market.
Monitoring and depth of strategy use in computer-based learning environments for science and history.
(2018)

BACKGROUND
Self-regulated learning (SRL) models position metacognitive monitoring as central to SRL processing and predictive of student learning outcomes (Winne & Hadwin, 2008; Zimmerman, 2000). A body of research evidence also indicates that depth of strategy use, ranging from surface to deep processing, is predictive of learning performance.


AIMS
In this study, we investigated the relationships among the frequency of metacognitive monitoring and the utilization of deep and surface-level strategies, and the connections between these SRL processes and learning outcomes across two academic domains, science and history.


SAMPLE
This was a secondary data analysis of two studies. The first study sample was 170 undergraduate students from a University in the south-eastern United States. The second study sample consisted of 40 US high school students in the same area.


METHODS
We collected think-aloud protocol SRL and knowledge measure data and conducted both structural equation modelling and path analysis to investigate our research questions.


RESULTS
Findings showed across both studies and two distinct academic domains, students who enacted more frequent monitoring also enacted more frequent deep strategies resulting in better performance on academic evaluations.


CONCLUSIONS
These findings suggest the importance of measuring not only what depth of strategies learners use, but also the degree to which they monitor their learning. Attention to both is needed in research and practice.
Question Type Classification Using a Part-of-Speech Hierarchy
(2011)

Question type (or answer type) classification is the task of determining the correct type of the answer expected to a given query. This is often done by defining or discovering syntactic patterns that represent the structure of typical queries of each type, and classify a given query according to which pattern they satisfy. In this paper, we combine the idea of using informer spans as patterns with our own part-of-speech hierarchy in order to propose both a new approach to pattern-based question type classification and a new way of discovering the informers to be used as patterns. We show experimentally that using our part-of-speech hierarchy greatly improves type classification results, and allows our system to learn valid new informers.
Dissociation between face perception and face memory in adults, but not children, with developmental prosopagnosia
(2014)

Cognitive models propose that face recognition is accomplished through a series of discrete stages, including perceptual representation of facial structure, and encoding and retrieval of facial information. This implies that impaired face recognition can result from failures of face perception, face memory, or both. Studies of acquired prosopagnosia, autism spectrum disorders, and the development of normal face recognition support the idea that face perception and face memory are distinct processes, yet this distinction has received little attention in developmental prosopagnosia (DP). To address this issue, we tested the face perception and face memory of children and adults with DP. By definition, face memory is impaired in DP, so memory deficits were present in all participants. However, we found that all children, but only half of the adults had impaired face perception. Thus, results from adults indicate that face perception and face memory are dissociable, while the results from children provide no evidence for this division. Importantly, our findings raise the possibility that DP is qualitatively different in childhood versus adulthood. We discuss theoretical explanations for this developmental pattern and conclude that longitudinal studies are necessary to better understand the developmental trajectory of face perception and face memory deficits in DP.
The public health risks of media violence: a meta-analytic review.
(2009)

OBJECTIVE
To conduct a meta-analytic review of studies that examine the impact of violent media on aggressive behavior and to determine whether this effect could be explained through methodological problems inherent in this research field.


STUDY DESIGN
A detailed literature search identified peer-reviewed articles addressing media violence effects. Effect sizes were calculated for all studies. Effect sizes were adjusted for observed publication bias.


RESULTS
Publication bias was a problem for studies of aggressive behavior, and methodological problems such as the use of poor aggression measures inflated effect size. Once corrected for publication bias, studies of media violence effects provided little support for the hypothesis that media violence is associated with higher aggression. The corrected overall effect size for all studies was r = .08.


CONCLUSIONS
Results from the current analysis do not support the conclusion that media violence leads to aggressive behavior. It cannot be concluded at this time that media violence presents a significant public health risk.
A UHF class E2 DC/DC converter using GaN HEMTs
(2012)

In this paper, the design of a class E2 resonant DC/DC converter, operating at UHF band, is proposed. Combining the use of GaN HEMT devices, both for the inverter and the synchronous rectifier, with high Q lumped-element multi-harmonic matching networks, a peak efficiency value of 72% has been obtained at 780 MHz with a 10.3 W output power. By means of a Pulse Width Modulation (PWM) over the gate driving envelope, the output voltage may be controlled while keeping low switching losses, with an estimated small-signal bandwidth (BW) and a slew rate of 11 MHz and 630 V/µSeg, respectively.
Vaping on Instagram: cloud chasing, hand checks and product placement.
(2016)

INTRODUCTION
This study documented images posted on Instagram of electronic cigarettes (e-cigarette) and vaping (activity associated with e-cigarette use). Although e-cigarettes have been studied on Twitter, few studies have focused on Instagram, despite having 500 million users. Instagram's emphasis on images warranted investigation of e-cigarettes, as past tobacco industry strategies demonstrated that images could be used to mislead in advertisements, or normalise tobacco-related behaviours. Findings should prove informative to tobacco control policies in the future.


METHODS
3 months of publicly available data were collected from Instagram, including images and associated metadata (n=2208). Themes of images were classified as (1) activity, for example, a person blowing vapour; (2) product, for example, a personal photo of an e-cigarette device; (3) advertisement; (4) text, for example, 'meme' or image containing mostly text and (5) other. User endorsement (likes) of each type of image was recorded. Caption text was analysed to explore different trends in vaping and e-cigarette-related text.


RESULTS
Analyses found that advertisement-themed images were most common (29%), followed by product (28%), and activity (18%). Likes were more likely to accompany activity and product-themed images compared with advertisement or text-themed images (p<0.01). Vaping-related text greatly outnumbered e-cigarette-related text in the image captions.


CONCLUSIONS
Instagram affords its users the ability to post images of e-cigarette-related behaviours and gives advertisers the opportunity to display their product. Future research should incorporate novel data streams to improve public health surveillance, survey development and educational campaigns.
Making Scheduling "Cool": Temperature-Aware Workload Placement in Data Centers
(2005)

Trends towards consolidation and higher-density computing configurations make the problem of heat management one of the critical challenges in emerging data centers. Conventional approaches to addressing this problem have focused at the facilities level to develop new cooling technologies or optimize the delivery of cooling. In contrast to these approaches, our paper explores an alternate dimension to address this problem, namely a systems-level solution to control the heat generation through temperatureaware workload placement. We first examine a theoretic thermodynamic formulation that uses information about steady state hot spots and cold spots in the data center and develop real-world scheduling algorithms. Based on the insights from these results, we develop an alternate approach. Our new approach leverages the non-intuitive observation that the source of cooling inefficiencies can often be in locations spatially uncorrelated with its manifested consequences; this enables additional energy savings. Overall, our results demonstrate up to a factor of two reduction in annual data center cooling costs over location-agnostic workload distribution, purely through software optimizations without the need for any costly capital investment.
Calorie restriction extends Saccharomyces cerevisiae lifespan by increasing respiration
(2002)

Calorie restriction (CR) extends lifespan in a wide spectrum of organisms and is the only regimen known to lengthen the lifespan of mammals. We established a model of CR in budding yeast Saccharomyces cerevisiae. In this system, lifespan can be extended by limiting glucose or by reducing the activity of the glucose-sensing cyclic-AMP-dependent kinase (PKA). Lifespan extension in a mutant with reduced PKA activity requires Sir2 and NAD (nicotinamide adenine dinucleotide). In this study we explore how CR activates Sir2 to extend lifespan. Here we show that the shunting of carbon metabolism toward the mitochondrial tricarboxylic acid cycle and the concomitant increase in respiration play a central part in this process. We discuss how this metabolic strategy may apply to CR in animals.
Internet of Things: A review to support IoT architecture's design
(2017)

Internet of Things (IoT) is a state-of-the-art field that is affecting every aspect of human life. This paper presents a state-of-the-art overview of IoT challenges, available standards, and popular architectures. The aim is to guide future researchers in the field of IoT by highlighting the main research gaps. Moreover, it provides research recommendations to help in designing future IoT architectures that fit IoT environment characteristics.
Sampling-oscilloscope measurement of a microwave mixer with single-digit phase accuracy
(2006)

We describe a straightforward method of separately characterizing up- and down-conversion in microwave mixers using a sampling oscilloscope. The method mismatch-corrects the results, determines both magnitude and phase, and uses a novel time-base correction scheme to improve the accuracy of the measurements. We estimate our measurement accuracy to be on the order of a tenth of a decibel in magnitude and a few degrees in phase. We use the method to characterize the magnitude and phase reciprocity of a microwave mixer.
Feedforward neural network and ANFIS-based approaches to forecasting the off-cam energy characteristics of Kaplan turbine
(2017)

The determination of the energy characteristics of a Kaplan hydraulic turbine is based on numerous measuring points during extensive and expensive experimental model tests in laboratory and on-site prototype tests at the hydropower plant. The results of those experimental researches are valuable insofar as they are detailed and comprehensive. In order to reduce the number of modes, in which the double-regulated turbine has to be tested with the aim of obtaining the off-cam energy characteristics in unknown operating modes, the application of contemporary artificial neural networks models is presented in the paper. The rationalization of the turbine test conditions may not be at the expense of the quality of the obtained characteristics. Two types of neural networks, feedforward neural networks and adaptive network-based fuzzy inference system with different partitioning methods, were used. The reliability of applied method was considered by analyzing and validating the predicted turbine energy parameters with the results obtained in the highly sophisticated laboratory.
Developing a data quality framework for asset management in engineering organisations
(2007)

Data Quality (DQ) is seen as critical to effective business decision-making. However, maintaining DQ is often acknowledged as problematic. Asset data is the key enabler in gaining control of assets. The quality asset data provides the foundation for effective Asset Management (AM). Researches have indicated that achieving AM DQ is the key challenge engineering organisations face today. This paper investigates the DQ issues emerging from the unique nature of engineering AM data. It presents an exploratory research of a large scale national-wide DQ survey on how Australian engineering organisations address DQ issues, and proposes an AM specific DQ framework.